Registry Event Log Warehouse: Toward Designing A Care Process Digital Twin

Roozbahani, Mohammad Hossein; Yari Eili, Mansoureh; Sharif-Alhoseini, Mahdi; HajiAliAsgari, Fatemeh

Volume 13, Issue 2 (6-2025) J Surg Trauma 2025, 13(2): 63-74 | Back to browse issues page

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Roozbahani M H, Yari Eili M, Sharif-Alhoseini M, HajiAliAsgari F. Registry Event Log Warehouse: Toward Designing A Care Process Digital Twin. J Surg Trauma 2025; 13 (2) :63-74
URL: http://jsurgery.bums.ac.ir/article-1-461-en.html

Registry Event Log Warehouse: Toward Designing A Care Process Digital Twin

Mohammad Hossein Roozbahani ^*

, Mansoureh Yari Eili

, Mahdi Sharif-Alhoseini

, Fatemeh HajiAliAsgari

Department of Nanotechnology, Faculty of Advanced Technology, Iran University of Science and Technology, Tehran, Iran

Abstract: (249 Views)

Introduction: Many data warehouses (DWs) developed for the healthcare field only represent subject-oriented data without addressing the control flow. Each process includes many ordered specific activities, which cannot be represented by DWs. This limitation, next to the need to find correlations between case and activity attributes in our health data, motivates the extension of a Registry Event Log Warehouse (REWH). Since the disease registry is saturated with valuable, critical data on human life and supports clinicians and professionals in their decision-making and patient monitoring, the present study aimed to develop a twin digital for trauma care as part of our overall work. Because the process warehouse is the first step in designing the trauma warehouse digital twin, the focus is developing a registry process warehouse.
Methods: The methodology comprises the following steps: 1) data gathering, 2) data preprocessing, 3) data entry to cube, and 4) conducting queries. The primary contribution of this work is to demonstrate how to gather heterogeneous data extracted from different sources into a single process-oriented repository and express online analytical processing (OLAP) queries through SQL to promote data filtering and aggregation.
Results: A top-bottom dimensional schemas of a process warehouse that support detailed process analysis, with each level represented by a specific granularity level.
Conclusion: Due to its ability to provide data of interest for all components of the digital twins, the DW is one of the primary components of healthcare digital twins. The multilevel analysis presented in this study, utilizing OLAP tools, can assist easy access to specific information from process warehouse dimensions almost instantly.

Keywords: Data Warehousing, Digital Health, Disease registries, Health Information Systems, Machine Learning